How are records stored in immudb

immudb is an open source Immutable Database that supports Cryptographical verification, tamper-resistance, and audit. It has support for both Key-Value and SQL and has high-performance and scalability solutions when compared to its competitors in the market.

Let’s dive deep into how a record is stored on disk by immudb, and the data structures and algorithms used internally.

Basic Building Blocks

immudb consists of the following data structures:

  • Append-only Logs
  • Merkle Trees
  • Timed B-Tree

Append-Only Logs

Append-only log, also called write-ahead log, is a family of techniques for providing atomicity and durability (two of the ACID properties) in database systems. An append-only log is an auxiliary disk-resident structure used for crash and transaction recovery. Append-only log files keep a record of data changes that occur by writing each change to the end of the file. In doing this, anyone could recover the entire dataset by replaying the append-only log from the beginning to the end. It is also fast, as compared to other database storage systems, because writes are only being appended to a file.

It is a fundamental building block for our immutable database system. Since new updates are layered over the previous ones, developers can time-travel and look into the past versions of a record.

Merkle tree

Merkle tree is an authenticated data structure organized as a tree.

Authenticated means the integrity of the data structure can be efficiently verified using the (Merkle) root of the tree. The dataset cannot be altered without changing the Merkle root. The underlying data structure is organized as a tree, where each parent node is obtained by hashing the data from the child nodes in the layer below.

https://ethereum.org/en/developers/tutorials/merkle-proofs-for-offline-data-integrity/

This is how a merkle tree is built:

  1. Individually hash each element of the original dataset into a leaf node.
  2. Hash together pairs of leaf nodes and store the resulting value in a parent branch node. The hashing function to obtain a leaf or branch node is different.
  3. Keep hashing pairs of branch nodes until you get to a single top branch node, the root of the tree.

The next post in this series is about the verification of data in immudb.

Use Case - Tamper-resistant Clinical Trials

Goal:

Blockchain PoCs were unsuccessful due to complexity and lack of developers.

Still the goal of data immutability as well as client verification is a crucial. Furthermore, the system needs to be easy to use and operate (allowing backup, maintenance windows aso.).

Implementation:

immudb is running in different datacenters across the globe. All clinical trial information is stored in immudb either as transactions or the pdf documents as a whole.

Having that single source of truth with versioned, timestamped, and cryptographically verifiable records, enables a whole new way of transparency and trust.

Use Case - Finance

Goal:

Store the source data, the decision and the rule base for financial support from governments timestamped, verifiable.

A very important functionality is the ability to compare the historic decision (based on the past rulebase) with the rulebase at a different date. Fully cryptographic verifiable Time Travel queries are required to be able to achieve that comparison.

Implementation:

While the source data, rulebase and the documented decision are stored in verifiable Blobs in immudb, the transaction is stored using the relational layer of immudb.

That allows the use of immudb’s time travel capabilities to retrieve verified historic data and recalculate with the most recent rulebase.

Use Case - eCommerce and NFT marketplace

Goal:

No matter if it’s an eCommerce platform or NFT marketplace, the goals are similar:

  • High amount of transactions (potentially millions a second)
  • Ability to read and write multiple records within one transaction
  • prevent overwrite or updates on transactions
  • comply with regulations (PCI, GDPR, …)


Implementation:

immudb is typically scaled out using Hyperscaler (i. e. AWS, Google Cloud, Microsoft Azure) distributed across the Globe. Auditors are also distributed to track the verification proof over time. Additionally, the shop or marketplace applications store immudb cryptographic state information. That high level of integrity and tamper-evidence while maintaining a very high transaction speed is key for companies to chose immudb.

Use Case - IoT Sensor Data

Goal:

IoT sensor data received by devices collecting environment data needs to be stored locally in a cryptographically verifiable manner until the data is transferred to a central datacenter. The data integrity needs to be verifiable at any given point in time and while in transit.

Implementation:

immudb runs embedded on the IoT device itself and is consistently audited by external probes. The data transfer to audit is minimal and works even with minimum bandwidth and unreliable connections.

Whenever the IoT devices are connected to a high bandwidth, the data transfer happens to a data center (large immudb deployment) and the source and destination date integrity is fully verified.

Use Case - DevOps Evidence

Goal:

CI/CD and application build logs need to be stored auditable and tamper-evident.
A very high Performance is required as the system should not slow down any build process.
Scalability is key as billions of artifacts are expected within the next years.
Next to a possibility of integrity validation, data needs to be retrievable by pipeline job id or digital asset checksum.

Implementation:

As part of the CI/CD audit functionality, data is stored within immudb using the Key/Value functionality. Key is either the CI/CD job id (i. e. Jenkins or GitLab) or the checksum of the resulting build or container image.

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